EU · in EnglishHORIZON-JU-EUROHPC-2026-QML-07-01

Quantum Machine Learning

The official English text of the call comes from the European Commission's Funding & Tenders portal.

Deadline

28 January 2027

Grant per project

2–3 million EURTotal budget: 6 million EUR · about 2 funded projects

Region

International (EU)

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Eligibility criteria

EU direct call: eligibility depends mainly on the consortium, the participants' countries and their types of organisation. We cannot check these from your company details, so we do not tell you whether you are eligible. The criteria are in the official call.

Official call

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What is this call about?

Ez az uniós pályázat a kvantumszámítástechnika és a mesterséges intelligencia integrációjára irányul, különösen adatintenzív és számításigényes feladatokhoz. A támogatás olyan kutatási és fejlesztési projektekre használható, amelyek kvantum gépi tanulási algoritmusokat, hibamitigation-technikákat és hibrid megoldásokat dolgoznak ki, valamint ezek gyakorlati alkalmazásait validálják az éghajlat-modellezés, gyógyszerfejlesztés, képfeldolgozás vagy pénzügyi elemzés terén. A pályázat elsősorban kutatóintézeteknek és innovatív vállalatoknak szól, amelyek Európa technológiai szuverenitásának erősítésében érdekeltek, és olyan megoldásokat szeretnének kifejleszteni, amelyek klasszikus módszerekhez képest mérhető előnyt nyújtanak.

Official description

Expected outcome and scope

Expected Outcome: Integration of quantum computing into data pre-processing pipelines and learning workflows for data-heavy or computationally intensive tasks, demonstrating clear improvements in processing speed, computational complexity, modelling accuracy, and reduced sample requirements at scales achievable with NISQ-era devices, Reliable and scalable Quantum Machine Learning (QML) models and algorithms, integrated with existing AI frameworks and pipelines, enabling faster data processing, improved prediction accuracy, and enhanced computational capabilities, Validated quantum-enhanced AI methods demonstrating measurable improvements over classical baselines in terms of speed, accuracy, data efficiency, complexity, or scalability, supported by rigorous benchmarking and complexity analysis, Robust, noise-aware QML techniques suitable for NISQ hardware , including error-mitigation strategies and algorithmic adaptations that improve reliability, performance, and reproducibility on real quantum processors, Demonstrators or proof-of-concept applications showcasing the relevance of QML for real-world challenges (e.g. climate and environmental modelling, Earth observation, healthcare and life sciences, materials discovery, finance, robotics, manufacturing, and cybersecurity), Strengthened European leadership and technological sovereignty in quantum computing and trustworthy AI, supported by cross-sector collaboration, knowledge transfer, and contributions to emerging standards, benchmarks and best practices.